Sentiment Analyzer
Commercial use OK
380+ models
No watermark
No sign-up needed
Model:
+ GPT-5, Claude, Gemini
Overall sentiment
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Polarity
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−100 to +100
Confidence
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Summary
Advanced options
Result
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Analyze text sentiment with free AI. Detect positive, negative, and neutral tones.
How to Use Sentiment Analyzer
1
Enter your input
Type text, upload a file, or describe what you want. No account needed.
2
Click generate
Our AI processes your request in seconds using the best open-source models.
3
Download & share
Download, copy, or share your result. Free for personal and commercial use.
Use this tool via API
Automate this tool from your own code. OpenAI-compatible REST endpoint, Bearer-token auth, no extra SDK required. Token costs match the web interface.
curl -X POST https://api.free.ai/v1/chat/ \
-H "Authorization: Bearer sk-free-..." \
-H "Content-Type: application/json" \
-d '{"model": "qwen7b", "messages": [{"role": "user", "content": "Summarize this: ..."}]}'Related Free AI Tools
Sentiment Analyzer — FAQ
Five things, depending on the mode you pick: (1) overall polarity — a -100 to +100 score with confidence; (2) 6-dimensional emotions — joy, anger, fear, sadness, surprise, disgust; (3) aspect-based — extracts named features or entities and scores each separately; (4) sarcasm / subtext — flips surface polarity on detected irony; (5) per-sentence — tags every sentence individually. Pick the mode that matches your use case.
Yes — a typical 200-word review runs the default Qwen 3 30B model at ~200 tokens, comfortably inside the 2,500 anonymous or 10,000 signed-up daily pool. Forensic-depth aspect analysis on longer texts costs ~600 tokens. No sign-up required to try a few.
Comparable on overall polarity (~85% agreement with human raters on clear-cut text), behind Google on subject-classification precision, ahead of most on sarcasm because we give the model explicit sarcasm-detection prompts. Enterprise vendors charge $1-4 per 1,000 units. Our advantage: emotion radar + aspect-based output in one call, free, no API key to provision.
Yes — switch to the "Sarcasm + subtext" mode. The system prompt explicitly instructs the model to flip surface polarity when it detects irony ("Oh great, another delay" scores negative despite positive-word presence). Accuracy on obvious sarcasm is ~80%; subtle passive-aggression is harder for any model. The "Mixed" verdict + low-confidence score is your flag to read the text yourself.
Instead of one verdict for the whole text, you get separate scores for each entity or feature mentioned. Example: "The hotel was beautiful but the breakfast was cold and the wifi was terrible" yields {hotel: +80, breakfast: -50, wifi: -70}. Ideal for product reviews, restaurant feedback, and NPS comments where customers mix praise and complaints in one paragraph.
99 languages. The six emotion dimensions come from Ekman basic-emotion research and translate universally. Accuracy is highest on English, Spanish, French, Portuguese, German, Chinese, Japanese; lower-resource languages work but may produce slightly lower confidence scores.
Yes — that is one of its highest-value use cases. Run incoming tickets through "Support ticket" domain mode; tickets scoring below -50 polarity with high anger-dimension go to your senior support queue, positive-verdict tickets route to self-service. Export JSON via the download button and wire into Zendesk / Freshdesk automations.
Yes — POST to /v1/chat/ with the same system prompt this page builds (inspect source for the exact prompt). Returns structured JSON. Good for dashboards that ingest thousands of reviews nightly. Bearer auth, monthly limits. Docs at /api/.
Confidence reflects the model certainty, not your certainty. Short inputs (<20 words), ambiguous text, and mixed-polarity content score low. A low-confidence positive is not a confident negative — it is "I do not know". Treat any score below 50 as needing human review.
The UI runs one text at a time. For bulk, the JSON+CSV download lets you wire the API into a Python or Node script that iterates your dataset. Limits: 20,000 characters per call, 100 calls/hour per IP for anonymous users, 1,000/hour for signed-up accounts. Enterprise rate limits available — contact us.
MonkeyLearn was acquired and discontinued. Lexalytics and Repustate charge $500-5,000/mo for equivalent aspect-based + emotion detection. Our tool is free for most workflows; premium models (GPT-5, Claude Sonnet) available per-call for the highest-stakes use cases like earnings-call transcripts.
Text is processed in-memory on our GPU, not persisted to disk for anonymous users. Signed-in users see reports in their dashboard for 7 days. We never share text with third parties or use it for training. For zero-log compliance use your own API-key-gated endpoint — contact us for enterprise SLAs.
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